Stochastic Optimization and Machine Learning Modeling for Wireless Networking

In the last years, the telecommunications industry has seen an increasing interest in the development of advanced solutions that enable communicating nodes to exchange large amounts of data. Indeed, well-known applications such as VoIP, audio streaming, video on demand, real-time surveillance systems, safety vehicular requirements, and remote computing have increased the demand for the efficient generation, utilization, management and communication of larger and larger data quantities. New transmission technologies have been developed to permit more efficient and faster data exchanges, including multiple input multiple output architectures or software defined networking: as an example, the next generation of mobile communication, known as 5G, is expected to provide data rates of tens of megabits per second for tens of thousands of users and only 1 ms latency. In order to achieve such demanding performance, these systems need to effectively model the considerable level of uncertainty related to fading transmission channels, interference, or the presence of noise in the data. In this thesis, we will present how different approaches can be adopted to model these kinds of scenarios, focusing on wireless networking applications. In particular, the first part of this work will show how stochastic optimization models can be exploited to design energy management policies for wireless sensor networks. Traditionally, transmission policies are designed to reduce the total amount of energy drawn from the batteries of the devices; here, we consider energy harvesting wireless sensor networks, in which each device is able to scavenge energy from the environment and charge its battery with it. In this case, the goal of the optimal transmission policies is to efficiently manage the energy harvested from the environment, avoiding both energy outage (i.e., no residual energy in a battery) and energy overflow (i.e., the impossibility to store scavenged energy when the battery is already full). In the second part of this work, we will explore the adoption of machine learning techniques to tackle a number of common wireless networking problems. These algorithms are able to learn from and make predictions on data, avoiding the need to follow limited static program instructions: models are built from sample inputs, thus allowing for data-driven predictions and decisions. In particular, we will first design an on-the-fly prediction algorithm for the expected time of arrival related to WiFi transmissions. This predictor only exploits those network parameters available at each receiving node and does not require additional knowledge from the transmitter, hence it can be deployed without modifying existing standard transmission protocols. Secondly, we will investigate the usage of particular neural network instances known as autoencoders for the compression of biosignals, such as electrocardiography and photo plethysmographic sequences. A lightweight lossy compressor will be designed, able to be deployed in wearable battery-equipped devices with limited computational power. Thirdly, we will propose a predictor for the long-term channel gain in a wireless network. Differently from other works in the literature, such predictor will only exploit past channel samples, without resorting to additional information such as GPS data. An accurate estimation of this gain would enable to, e.g., efficiently allocate resources and foretell future handover procedures. Finally, although not strictly related to wireless networking scenarios, we will show how deep learning techniques can be applied to the field of autonomous driving. This final section will deal with state-of-the-art machine learning solutions, proving how these techniques are able to considerably overcome the performance given by traditional approaches

Beam scanning by liquid-crystal biasing in a modified SIW structure

A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

An Algorithmic Interpretation of Quantum Probability

The Everett (or relative-state, or many-worlds) interpretation of quantum mechanics has come under fire for inadequately dealing with the Born rule (the formula for calculating quantum probabilities). Numerous attempts have been made to derive this rule from the perspective of observers within the quantum wavefunction. These are not really analytic proofs, but are rather attempts to derive the Born rule as a synthetic a priori necessity, given the nature of human observers (a fact not fully appreciated even by all of those who have attempted such proofs). I show why existing attempts are unsuccessful or only partly successful, and postulate that Solomonoff's algorithmic approach to the interpretation of probability theory could clarify the problems with these approaches. The Sleeping Beauty probability puzzle is used as a springboard from which to deduce an objectivist, yet synthetic a priori framework for quantum probabilities, that properly frames the role of self-location and self-selection (anthropic) principles in probability theory. I call this framework "algorithmic synthetic unity" (or ASU). I offer no new formal proof of the Born rule, largely because I feel that existing proofs (particularly that of Gleason) are already adequate, and as close to being a formal proof as one should expect or want. Gleason's one unjustified assumption--known as noncontextuality--is, I will argue, completely benign when considered within the algorithmic framework that I propose. I will also argue that, to the extent the Born rule can be derived within ASU, there is no reason to suppose that we could not also derive all the other fundamental postulates of quantum theory, as well. There is nothing special here about the Born rule, and I suggest that a completely successful Born rule proof might only be possible once all the other postulates become part of the derivation. As a start towards this end, I show how we can already derive the essential content of the fundamental postulates of quantum mechanics, at least in outline, and especially if we allow some educated and well-motivated guesswork along the way. The result is some steps towards a coherent and consistent algorithmic interpretation of quantum mechanics